Top Machine Learning Jobs in Minneapolis, MN

Provide legal counsel on AI and advanced technology initiatives across UnitedHealth Group, supporting development, deployment, governance, contracting, and regulatory responses. Partner with technical and business stakeholders, advise the AI Review Board, maintain templates/playbooks, and support technology-related transactions and operations.

Lead and mentor multiple engineering teams to deliver AI-enabled MSK platform solutions. Drive AI strategy, design and deploy responsible ML solutions, ensure regulatory (CMS) compliance, oversee vendor partners, modernize platform architecture, and manage production support and incident governance.

Lead AMP's multidisciplinary engineering organization to define and execute technology vision and roadmap. Responsible for scaling teams across hardware, software, AI, and infrastructure; driving product development, quality systems, and reliable deployments; partnering with execs and cross-functional teams on budgets, prioritization, and metrics; recruiting and mentoring leaders; and balancing short-term delivery with long-term investments to create business value.

Lead design, deployment, and operation of production ML systems and inference pipelines. Build and scale APIs and services on Kubernetes, optimize pipelines, monitor model performance and drift, and partner with cross-functional teams to deliver ML insights to customers.

Design and implement AI/ML systems for healthcare, own inference and delivery, manage/scale production ML systems, build scalable data pipelines and APIs, apply MLOps practices (deployment, monitoring, drift detection), and work with large-scale data and distributed computing frameworks.

Lead the design, development, and deployment of advanced machine learning solutions at Optum Tech, mentoring junior engineers and collaborating across teams.

Partner with healthcare clients to design, prototype, and deploy production-grade AI/LLM solutions. Lead discovery, architecture, cloud integration, and delivery while troubleshooting distributed systems and driving measurable client outcomes.

Lead cloud migrations for voice contact center solutions, design AI/ML capabilities, improve automation and governance, and ensure production readiness while collaborating with cross-functional teams.

Oversee AI/ML project development, mentor teams, develop machine learning models, and promote AI solutions for healthcare optimization.

Lead design, build, and deploy production-grade Voice AI and LLM capabilities (ASR, real-time transcription, bilingual switching). Set architecture and enterprise standards, mentor engineers, ensure HIPAA-compliant, secure, and reusable solutions, and align roadmaps with business partners and vendors.

Design, build, and deploy production-grade ML and AI solutions for behavioral health. Architect end-to-end MLOps pipelines, optimize and monitor models in production, evaluate emerging AI trends, collaborate cross-functionally, and provide technical leadership and mentorship to engineering teams while ensuring responsible, scalable, and secure AI deployments.

Design, build, and deploy LLM-based ML solutions using LangChain, fine-tuning, and RAG. Write production-quality Python code (PEP8), containerize with Docker, use Git, test and document work, and apply traditional ML methods like XGBoost and K-means.

Design, implement, and productionize LLM-based ML solutions using LangChain, fine-tuning, and RAG. Write clean Python code (PEP8), containerize with Docker, use version control, test and document, and apply traditional ML methods like XGBoost and K-means.

Support risk and analytics efforts by performing SQL/Python-based analysis, monitoring portfolio health, building scalable reports, collaborating cross-functionally, and integrating AI tools to surface insights that inform product, risk, and growth decisions.

Develop novel AI approaches for healthcare using structured and unstructured data, build ML models, conduct experiments, and collaborate with stakeholders.

Design, develop, and deploy Generative AI and ML solutions (LLMs, RAG, agentic frameworks) for healthcare claims adjudication. Build end-to-end pipelines, apply MLOps, ensure LLM security and responsible AI, deploy on cloud platforms, integrate data warehousing, collaborate with product and platform teams, and mentor junior engineers.

Develop AI approaches using structured and unstructured data, build ML models, perform statistical analysis, and collaborate with stakeholders and experts.

Design, build, evaluate, and deploy AI/LLM-powered solutions (RAG, agents, classifiers, summarizers) for consumer-facing products. Develop prompt strategies, guardrails, and evaluation frameworks; partner with data engineering and cross-functional teams to ensure trusted data, observability, safety, and continuous improvement of model performance in production.

Lead applied AI/ML initiatives, design and deliver production-grade software, develop secure high-quality code, troubleshoot, automate remediation, evaluate vendor architectures, champion best practices across engineering, and drive adoption of AI/ML, NLP, LLMs and streaming technologies.

Research and develop LLM and NLP solutions (including agentic AI) for healthcare, collaborate with RUAI and engineering teams, build MLOps pipelines, document code and models, publish/patent novel methods, and ensure solutions comply with company AI policies.

Lead strategy and execution of enterprise-scale AI/ML and generative AI solutions for claims, eligibility, and benefits. Define AI engineering roadmap, build LLM and agentic systems, scale cloud-native AI workloads, enforce responsible AI governance, partner with product and business stakeholders, manage budgets, and hire and mentor a global engineering organization to deliver measurable business outcomes.

Design, build, and deploy production-grade AI/ML and LLM solutions for claims processing, eligibility, prior authorization, and payment integrity. Build scalable data pipelines, integrate models with core payer systems, ensure HIPAA/PHI-safe pipelines and Responsible AI governance, monitor and retrain models, and partner with cross-functional teams to deliver reliable, CI/CD-driven solutions.

Design, build, and deploy scalable AI/ML models and pipelines for consumer healthcare. Develop production code (Python/Java/Scala), automate infrastructure (Terraform, Kubernetes), validate model performance, integrate cloud services (Azure/AWS), and collaborate cross-functionally to monitor and document enterprise-scale AI solutions.

Design, build, and deploy scalable data pipelines and AI/ML solutions using LLMs and agentic frameworks. Implement prompt engineering, fine-tune models, integrate AI workflows in cloud environments, ensure healthcare compliance and Responsible AI, and mentor junior engineers to deliver automated claims adjudication and other production AI systems.

Own end-to-end analytical lifecycle to develop and optimize business, risk, and growth strategies. Monitor portfolio and macro trends, build and interpret ML-driven models, lead cross-functional launches, mentor junior analysts, and build AI-assisted workflows to improve decisioning and measurement across products and geographies.